The discriminator, trained on millions of real landscape images, gives the generator network pixel-by-pixel feedback on how to make the synthetic images more realistic.
AI Behind the GauGAN2 DemoĪt the heart of GauGAN2 are generative adversarial networks, or GANs - a kind of deep learning model that involves a pair of neural networks: a generator and a discriminator. Clicking on a filter (or uploading a custom image) allows users to experiment with different lighting or apply a specific painting style to their creations. By tweaking the text to a “lake in front of snowy mountain” or “ forest in front of mountain,” the AI model instantly modifies the image.Īrtists who prefer to draw a scene themselves can use the demo’s smart paintbrush to modify these text-prompted scenes or start from scratch, drawing in boulders, trees or fluffy clouds. Users can simply type a phrase like “lake in front of mountain” and press a button to generate a scene in real time. The latest version of the demo, GauGAN2, turns any combination of words and drawings into a lifelike image. Named after post-Impressionist painter Paul Gauguin, it was created by NVIDIA Research and can be experienced free through NVIDIA AI Demos.
This technology is powered by the 2015 acquisition of Looksery, a Ukranian company with patents on using machine learning to track movements in video.GauGAN, an AI demo for photorealistic image generation, allows anyone to create stunning landscapes using generative adversarial networks.
Uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace “lol”) Machine learning is a type of artificial intelligence where we teach machines how to solve problems without explicitly programming them to do so.Īutomatically identify objects in images (or “pins”) Artificial Intelligence and Machine LearningĪrtificial Intelligence is a field of computer science that attempts to create machines that act rationally in response to their environment.
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